7th International Conference on Spoken Language Processing
September 16-20, 2002
In this paper, we propose to use neighborhood information inmodel space to perform utterance verification (UV). At first, we present a nested-neighborhood structure for each underlying model in model space and assume the underlying modelís competing models sit in one of these neighborhoods, which is used to model alternative hypothesis in UV. Bayes factors (BF) is first introduced to UV and used as a major tool to calculate confidence measures based on the above idea. Experimental results in Bell Labs communicator system show that the new method has dramatically improved verification performance when verifying correct words against misrecognized words in recognizerís output, relatively more than 20% reduction in equal error rate (EER) when comparing with the standard approach based on likelihood ratio testing and anti-models.
Bibliographic reference. Jiang, Hui / Lee, Chin-Hui (2002): "Utterance verification based on neighborhood information and Bayes factors", In ICSLP-2002, 1605-1608.